In some example embodiments, a computer-implemented method may include training a machine learning model in a first database instance using a machine learning algorithm and a training dataset in response to receiving a request to train, serializing the trained machine learning model into a binary file in response to the training of the machine learning model, recreating the trained machine learning model in a second database instance using the binary file in response to receiving a request to apply the machine learning model, and generating an inference result by applying the recreated trained machine learning model on the inference dataset in the second database instance.
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2. The computer-implemented method of claim 1, wherein the machine learning algorithm comprises a linear regression algorithm, a logistical regression algorithm, a gradient boosting algorithm, a neural network algorithm, or a decision tree learning algorithm.
This invention relates to computer-implemented methods for predictive modeling using machine learning algorithms. The method addresses the challenge of selecting an appropriate machine learning algorithm for a given predictive modeling task, where different algorithms have varying strengths and weaknesses depending on the data characteristics and problem requirements. The method involves applying a machine learning algorithm to a dataset to generate predictions. The algorithm may be a linear regression algorithm, which models the relationship between a dependent variable and one or more independent variables using a linear equation. Alternatively, it may be a logistic regression algorithm, which estimates probabilities using a logistic function, making it suitable for binary classification tasks. Another option is a gradient boosting algorithm, which builds an ensemble of weak learners in a sequential manner, improving predictive accuracy through iterative corrections. A neural network algorithm may also be used, which employs interconnected layers of nodes to learn complex patterns from data. Finally, a decision tree learning algorithm can be applied, which constructs a tree-like model of decisions and their possible consequences, useful for both classification and regression tasks. The method ensures flexibility in algorithm selection, allowing users to choose the most suitable approach based on the specific requirements of their predictive modeling task. This adaptability enhances the system's ability to handle diverse datasets and problem types effectively.
3. The computer-implemented method of claim 1, wherein the training configuration further indicates one or more hyperparameters, the one or more hyperparameters being used in the training of the machine learning model.
4. The computer-implemented method of claim 1, wherein the binary file comprises model parameters of the trained machine learning model.
5. The computer-implemented method of claim 4, wherein the model parameters of the trained machine learning model comprise weights for a neural network model, coefficients for a linear regression model, or rules for a decision tree model.
6. The computer-implemented method of claim 1, wherein the binary file comprises an identification of the machine learning algorithm.
The invention relates to computer-implemented methods for processing binary files containing machine learning algorithms. The problem addressed is the need to efficiently identify and manage machine learning algorithms embedded within binary files, which is crucial for tasks such as deployment, verification, and compatibility checks in software systems. The method involves analyzing a binary file to extract an identification of the machine learning algorithm it contains. This identification may include metadata, version information, or other distinguishing features that specify the algorithm's type, configuration, or source. The extracted identification is then used to determine the algorithm's properties, such as its computational requirements, input/output specifications, or compatibility with other system components. This allows for automated decision-making, such as selecting appropriate hardware resources, optimizing execution parameters, or ensuring compliance with system requirements. The method may also involve comparing the extracted identification against a database of known algorithms to verify authenticity, detect anomalies, or enforce security policies. Additionally, the identification can be used to generate reports or logs for auditing purposes, tracking algorithm usage, or maintaining regulatory compliance. The approach ensures that machine learning algorithms are correctly recognized and managed within binary files, improving system reliability and operational efficiency.
7. The computer-implemented method of claim 1, wherein the binary file comprises one or more hyperparameters of the trained machine learning model.
8. The computer-implemented method of claim 1, further comprising storing the binary file in a persistence layer of the application platform, wherein the recreating of the trained machine learning model comprises accessing the stored binary file.
This invention relates to machine learning model deployment in application platforms. The problem addressed is the efficient storage and retrieval of trained machine learning models to enable their recreation without retraining. The method involves converting a trained machine learning model into a binary file format, which is then stored in a persistence layer of the application platform. When the model needs to be recreated, the system accesses the stored binary file to reconstruct the trained model. This approach ensures that the model can be quickly reloaded without requiring the computationally expensive retraining process. The persistence layer provides a reliable storage mechanism, allowing the binary file to be accessed as needed for model recreation. This method is particularly useful in environments where model retraining is impractical due to resource constraints or time limitations. By storing the model in a binary format, the system ensures compatibility and consistency across different deployment scenarios. The invention optimizes the deployment pipeline by reducing latency and resource usage associated with model recreation.
9. The computer-implemented method of claim 1, wherein the function comprises causing the inference result to be displayed on the computing device.
This invention relates to computer-implemented methods for processing and displaying inference results, particularly in systems where machine learning or data analysis models generate outputs that need to be presented to users. The problem addressed is the efficient and accurate presentation of inference results, ensuring they are displayed in a clear and actionable manner on a computing device. The method involves executing a function that processes an inference result, which is typically generated by a machine learning model or an analytical algorithm. The function includes steps to format, transform, or otherwise prepare the inference result for display. This may involve converting raw data into a human-readable format, applying visual enhancements, or organizing the output in a structured manner. The function then causes the processed inference result to be displayed on a computing device, such as a smartphone, tablet, or computer. The display may include graphical representations, text, or interactive elements to facilitate user interaction with the results. The method ensures that the inference result is presented in a way that is easily interpretable and useful for decision-making or further analysis.
11. The system of claim 10, wherein the machine learning algorithm comprises a linear regression algorithm, a logistical regression algorithm, a gradient boosting algorithm, a neural network algorithm, or a decision tree learning algorithm.
12. The system of claim 10, wherein the training configuration further indicates one or more hyperparameters, the one or more hyperparameters being used in the training of the machine learning model.
The invention relates to a system for training machine learning models, specifically addressing the need for configurable training processes to optimize model performance. The system includes a training configuration that defines parameters and settings for the training process, ensuring adaptability to different machine learning tasks. A key feature is the inclusion of hyperparameters within the training configuration, which are critical variables that influence the model's learning process. These hyperparameters are used to control aspects such as learning rate, batch size, or regularization strength, allowing fine-tuning of the model's behavior during training. The system also incorporates a data processing module to prepare input data for training, ensuring consistency and quality. Additionally, a model training module executes the training process using the specified hyperparameters and configuration, generating a trained machine learning model. The system may further include a model evaluation module to assess the model's performance, providing feedback for further optimization. This approach enables efficient and customizable training of machine learning models across various applications.
13. The system of claim 10, wherein the binary file comprises model parameters of the trained machine learning model.
A system for managing machine learning models includes a binary file containing model parameters of a trained machine learning model. The system is designed to facilitate the deployment, storage, and execution of machine learning models in a computing environment. The binary file format allows for efficient storage and transmission of the model parameters, which are the learned weights, biases, and other configurations that define the behavior of the trained model. This system may be part of a larger framework that handles model versioning, deployment, and inference tasks. The binary file may also include metadata such as model architecture details, training configurations, or performance metrics to ensure proper integration and execution of the model in different environments. The system ensures that the model parameters are accurately preserved and can be reliably loaded for inference or further training. This approach optimizes resource usage and simplifies the management of machine learning models in production systems.
14. The system of claim 13, wherein the model parameters of the trained machine learning model comprise weights for a neural network model, coefficients for a linear regression model, or rules for a decision tree model.
This invention relates to a machine learning system designed to optimize model parameters for improved predictive performance. The system addresses the challenge of selecting and tuning model parameters to enhance accuracy, efficiency, or interpretability in machine learning applications. The core system includes a training module that processes input data to generate a trained machine learning model, where the model parameters are adjusted based on performance metrics. These parameters can include weights for neural networks, coefficients for linear regression models, or rules for decision trees, depending on the model type. The system further incorporates a validation module to evaluate the trained model using a separate dataset, ensuring robustness and generalization. Additionally, a hyperparameter optimization module fine-tunes the model's configuration to maximize performance. The system may also include a deployment module to integrate the trained model into real-world applications, enabling automated decision-making or predictive analytics. By dynamically adjusting model parameters and validating performance, the system improves the reliability and adaptability of machine learning models across various domains.
15. The system of claim 10, wherein the binary file comprises an identification of the machine learning algorithm.
16. The system of claim 10, wherein the binary file comprises one or more hyperparameters of the trained machine learning model.
17. The system of claim 10, wherein the operations further comprise storing the binary file in a persistence layer of the application platform, wherein the recreating of the trained machine learning model comprises accessing the stored binary file.
18. The system of claim 10, wherein the function comprises causing the inference result to be displayed on the computing device.
This invention relates to a system for processing and displaying inference results in a computing environment. The system addresses the challenge of efficiently generating and presenting inference outcomes, such as those derived from machine learning models or analytical computations, to users in a clear and actionable manner. The system includes a computing device configured to execute a function that processes input data to produce an inference result. The function may involve running a trained model, performing statistical analysis, or executing a rule-based algorithm. The system further includes a display module that causes the inference result to be presented on the computing device's interface, ensuring the output is accessible and interpretable by the user. The display module may format the result for readability, integrate it into a user interface, or trigger additional actions based on the inference outcome. The system may also include a data input module to receive and preprocess input data before inference, and a communication interface to transmit results to other devices or systems. The display module can adapt the presentation based on the type of inference result, such as visualizing numerical outputs, categorizing predictions, or highlighting key insights. This ensures users can quickly understand and act on the generated results, improving decision-making efficiency in applications like diagnostics, recommendations, or monitoring systems.
20. The non-transitory machine-readable storage medium of claim 19, wherein the function comprises causing the inference result to be displayed on the computing device.
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May 8, 2020
November 8, 2022
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